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Study on automatic detection and classification of breast nodule using deep convolutional neural network system.

Feiqian Wang1, Xiaotong Liu2,3, Na Yuan1

  • 1Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, China.

Journal of Thoracic Disease
|November 4, 2020
PubMed
Summary

Automated Breast Ultrasound (ABUS) combined with deep convolutional neural networks (CNNs) offers a promising approach for detecting and classifying breast nodules. This AI-powered system aims for more accurate and efficient breast cancer diagnosis.

Keywords:
Deep convolutional neural networks (CNN)automated breast ultrasound (ABUS)breast nodulecomputer-aided diagnosis (CAD)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Conventional breast ultrasound methods are operator-dependent, slow, and prone to errors.
  • Automated Breast Ultrasound (ABUS) and Deep Learning (DL) offer potential solutions for improving diagnostic accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate an automated system for breast nodule detection and classification using ABUS and CNNs.
  • To achieve accurate and efficient diagnosis of breast nodules, overcoming limitations of manual methods.

Main Methods:

  • A dataset of 293 lesions from 194 patients was used, with scans performed using ABUS.
  • A 3D U-Net architecture was employed for nodule detection, optimized with residual blocks and attention mechanisms.
  • A classification model using convolutional and fully-connected layers was developed to differentiate between benign and malignant nodules.

Main Results:

  • The detection model achieved 91% sensitivity with 1.92 false positives per scan.
  • The classification model demonstrated 87.0% sensitivity, 88.0% specificity, and 87.5% accuracy.
  • Performance was evaluated against previous studies and within a dedicated test set.

Conclusions:

  • The combination of deep CNNs and ABUS shows potential as a tool for the easy and accurate diagnosis of breast nodules.
  • This AI-driven approach may enhance the reliability and efficiency of breast cancer screening and diagnosis.